Author(s)

PeiChen Wu¹, DengBin Xu¹, LiNing Yuan^2*

Affiliation(s)

¹ School of Information Network Security, People's Public Security University of China, Beijing 100038, China.

² School of Public Security Big Data Modern Industry, Guangxi Police College, Nanning 530028, Guangxi, China.

Corresponding Author

LiNing Yuan

ABSTRACT

Video abnormal behavior detection is a research hotspot in the field of computer vision. By extracting the spatiotemporal characteristics of video content, we can determine whether there are abnormal events and their types in the video, and identify the location and time of the abnormal events. Based on supervised/unsupervised learning, this paper systematically combs and summarizes the existing video abnormal behavior detection methods. Starting from the current mainstream modeling idea, the supervision method is described in detail, and the completely unsupervised method is introduced to train the model. The network architectures of different models are compared, and the characteristics of various anomaly detection models in terms of test data sets, usage scenarios, advantages and limitations are summarized. Then, through common evaluation criteria such as frame level standard and pixel level standard, the model is compared and the performance is evaluated. At the same time, the performance of different methods is compared within the class, and the results are analyzed and summarized in depth. Finally, the future development direction is outlined briefly, and the development trend of video anomaly detection from virtual composite dataset, multi-modal large-scale model to lightweight model is discussed.

KEYWORDS

Abnormal behavior detection; Deep learning; Fully unsupervised; Multimodal features

CITE THIS PAPER

PeiChen Wu, DengBin Xu, LiNing Yuan. Theory and applications of video abnormal behavior detection. Journal of Computer Science and Electrical Engineering. 2024, 6(2): 29-45. DOI: 10.61784/jcsee3006.

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